Use of products for detecting the level of metabolites in serum for the preparation of a medicament for diagnosing patients with each type of SLE and preparation of a medicament

Abstract

The application discloses application of a product for detecting metabolite levels in serum in preparation of a preparation for diagnosing systemic lupus erythematosus (SLE) and SLE patients with different organ involvement phenotypes and a reagent. The application first uses specific serum biomarkers to assist in screening SLE, SLE with different disease activity and SLE patients with different organ phenotypes, including only kidney involvement systemic lupus erythematosus (KI), only skin involvement systemic lupus erythematosus (SI), only blood system involvement systemic lupus erythematosus (BI) and multi-system involvement systemic lupus erythematosus (MI), thereby opening up a new way for the diagnosis of the field and helping to more accurately diagnose and evaluate the disease progression of SLE. The blood specimen required is small, easy to carry out and has a good application prospect.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of medical molecular biology detection, and particularly relates to application of a product for detecting a diagnostic marker level of SLE, SLE of different disease activity, and SLE of different organ involvement in preparation of a reagent for diagnosing each type of SLE patient and a preparation thereof. BACKGROUND

[0002] Systemic lupus erythematosus (SLE) is an autoimmune disease that predominantly affects women of childbearing age. It is characterized by the presence of a large number of antibodies against multiple self-antigens in the body, which directly lead to pathological changes of SLE with some self-reactive T cells. The severity of SLE depends on the degree of involvement of major organs, including the most common skin, kidney, blood system, joints, central nervous system, etc. Common skin manifestations include discoid erythema on the face, butterfly erythema on the cheeks, photosensitivity, alopecia, etc. 30-60% of SLE patients will progress to lupus nephritis, which is one of the most important causes of SLE death. Early manifestations include glomerular and nephrotic syndrome, such as hematuria, proteinuria, and tubular urine. Renal biopsy is currently the gold standard for diagnosis. Up to 90% of SLE patients will have symmetrical arthralgia. In addition, SLE patients will also have blood system involvement, manifested as leukopenia, anemia, thrombocytopenia, or all three. Although it is generally believed that genetic, hormonal, environmental, and immunological factors are related to the various clinical manifestations of SLE patients. However, the exact pathogenesis of SLE is still unclear, and the current diagnosis of SLE is mainly based on typical clinical manifestations, including signs and symptoms from multiple organ systems, and the presence of autoantibodies. Due to the wide variety and heterogeneity of clinical symptoms of SLE, the diagnosis of autoantibodies has low sensitivity or specificity, and so far there is no early and accurate clinical marker for the diagnosis and classification of systemic lupus erythematosus.

[0003] Metabolites, as direct reflectors of phenotypes of organisms, are a natural phenotype indicator. Therefore, metabolite analysis has become an effective method widely used in clinical diagnosis. In recent years, the rapid development of high-throughput methods has enabled better identification and characterization of biomarkers for complex diseases, including SLE. By combining LC-MS / MS metabolomics and lipidomics methods, the coverage of metabolites in serum has been increased. Given the pathological differences between SLE of different disease activity and SLE of different organ involvement phenotypes, the combination of metabolomics and lipidomics may become an important platform for discovering biomarkers for different clinical phenotypes of SLE.

[0004] The severity of SLE is closely related to the type of involved organs. Therefore, finding reliable biomarkers for SLE of different organ involvement will significantly benefit the optimal treatment of SLE patients. Summary of the Invention

[0005] This invention discovers biomarkers for the early diagnosis of SLE, SLE with different disease activities, and SLE involving different organs. By detecting the levels of different metabolites, and further combining logistic binary regression and ROC curve analysis, it can assist in the early diagnosis of SLE, SLE with different disease activities, and can also distinguish SLE involving different organs, including SLE with only kidney involvement (KI), SLE with only skin involvement (SI), SLE with only hematologic system involvement (BI), and SLE with multiple systems involvement (MI).

[0006] The primary objective of this invention is to utilize the product, which detects multiple metabolite levels, in the preparation of formulations for the auxiliary diagnosis of SLE, SLE with different disease activities, and SLE with different organ involvement.

[0007] The application of products for detecting serum metabolite levels in the preparation of diagnostic agents for various types of SLE patients, including at least one of SLE, SLE with different disease activities, and SLE with different organ involvement (KI, SI, BI, MI); the four biomarkers used for diagnosing SLE patients are: dehydroepiandrosterone sulfate, 2-methylbutyrylglycine, benzoic acid, and fatty acid (FA) (20:1); the three biomarkers used for diagnosing SLE with different disease activities are: lysophosphatidylcholine (LPC) (18:0), phosphatidylcholine (PC) (18:3 / 18:3), and phosphatidylethanolamine (PE) (16:0 / 22:4); the SLE patients with different organ involvement include: KI, SI, BI, MI; the four biomarkers for diagnosing KI patients are: 2-hydroxyethanesulfonate, 5,8,11-eicosatetrienoic acid, pyrazine, PE (18:1e / 21:2); the two biomarkers for diagnosing SI patients are: L-isoleucine, triacylglycerol (TAG) (12:3 / 21:3 / 21:3); the five biomarkers for diagnosing BI patients are: cis-5-tetradecanoic acid carnitine, LPC (22:6(4Z,7Z,10Z,13Z,16Z,19Z)), PC (14:0 / 18:2), PC (16:1e / 18:2), PE (10:0 / 26:4); the six biomarkers for diagnosing MI patients are: L-α-aspartic acid-L-hydroxyproline, PE(16:0 / 18:2), PC(15:0 / 18:2(9Z,12Z)), PC(16:0 / P-16:0), PC(22:5(4Z,7Z,10Z,13Z,16Z) / 14:0), PC(5:0 / 26:1).

[0008] In the aforementioned application, during the diagnosis of SLE patients, at least one of four biomarkers is used. In SLE patients, the level of dehydroepiandrosterone sulfate is decreased compared with HC, while the levels of FA (20:1), 2-methylbutyrylglycine, and benzoic acid are increased compared with HC.

[0009] In this application, when diagnosing SLE patients with different disease activities, at least one of three biomarkers is used to assess disease activity using SLEDAI. The PE (16:0 / 22:4) level is higher in active SLE patients compared to inactive SLE patients, while the LPC (18:0) and PC (18:3 / 18:3) levels are lower in active SLE patients.

[0010] In the aforementioned application, during the diagnosis of KI patients, at least one of the four biomarkers was used. The levels of 2-hydroxyethanesulfonate, 5,8,11-eicosatotrienoic acid, pyrazine, and PE (18:1e / 21:2) in KI patients were elevated compared with those in HC. Furthermore, the levels of 2-hydroxyethanesulfonate, 5,8,11-eicosatotrienoic acid, pyrazine, and PE (18:1e / 21:2) did not show significant differences in pairwise comparisons between SI, BI, MI patients and HC.

[0011] In the aforementioned application, during the diagnosis of SI patients, at least one of two biomarkers was used. The levels of L-isoleucine and TAG (12:3 / 21:3 / 21:3) in SI patients were lower than those in HC patients. Furthermore, there were no significant differences in the levels of L-isoleucine and TAG (12:3 / 21:3 / 21:3) between KI, BI, MI patients and HC patients in pairwise comparisons.

[0012] In the aforementioned application, during the diagnosis of BI patients, at least one of the five biomarkers was used. Compared with HC, the levels of PC (14:0 / 18:2), PC (16:1e / 18:2), and PE (10:0 / 26:4) in BI patients were decreased, while the levels of cis-5-tetradecanoic acid carnitine and LPC (22:6(4Z,7Z,10Z,13Z,16Z,19Z)) were increased in BI patients. Furthermore, the levels of cis-5-tetradecanoic acid carnitine, LPC (22:6(4Z,7Z,10Z,13Z,16Z,19Z)), PC (14:0 / 18:2), PC (16:1e / 18:2), and PE (10:0 / 26:4) did not show significant differences in the levels of KI, SI, MI patients and HC patients in pairwise comparisons.

[0013] In the aforementioned application, during the diagnosis of MI patients, at least one of six biomarkers is used. MI patients show decreased PC (16:0 / P-16:0) and PC (22:5(4Z,7Z,10Z,13Z,16Z) / 14:0) levels compared to HC, and increased L-α-aspartate-L-hydroxyproline, PE (16:0 / 18:2), PC (15:0 / 18:2(9Z,12Z)), and PC (5:0 / 26:1) levels compared to HC. Furthermore, L-α-aspartate-L-hydroxyproline, PE (16:0 / 18:2), PC (15:0 / 18:2(9Z,12Z)), PC (16:0 / P-16:0), and PC (5:0 / 26:1) levels are also observed. PC(22:5(4Z,7Z,10Z,13Z,16Z) / 14:0) levels showed no significant differences in pairwise comparisons between KI, SI, BI patients and HC patients.

[0014] The application described herein involves analyzing the test results of multiple samples to obtain metabolite levels. Univariate statistical analysis, including Student's t-test and multivariate analysis, is used to screen for differential metabolites and lipids among OPLS-DA screening groups. A diagnostic model is established using binary logistic regression and ROC analysis to derive a formula for the diagnosis of the sample to be tested.

[0015] In the aforementioned application, compared to HC patients with SLE,

[0016] A binary logistic regression analysis was performed on the levels of four metabolites, yielding the formula Logit(P)=Log (P / (1-P))=-0.794+34223.471A+178897.26B–2009.888C+116698.988D. The predicted cutoff value for P was 0.81. When P≥0.81, it was classified as SLE, and when P<0.81, it was classified as HC. Where A represents the serum content of 2-methylbutyrylglycine; B represents the serum content of benzoic acid; C represents the serum content of dehydroepiandrosterone sulfate; and D represents the serum content of fatty acids (20:1).

[0017] In the aforementioned application, compared with inactive SLE patients, a binary logistic regression analysis was performed using the levels of three metabolites, yielding the formula Logit(P)=Log(P / (1-P))=2.379-28127.018E-37622.689F+4938.163G. The cutoff value for predicting P was 0.5. When P≥0.5, patients were classified as having active SLE, and when P<0.5, they were classified as having inactive SLE. Where E represents the serum content of lysophosphatidylcholine (18:0); F represents the serum content of phosphatidylcholine (18:3 / 18:3); and G represents the serum content of phosphatidylethanolamine (16:0 / 22:4).

[0018] In the aforementioned application, KI patients were compared with non-KI patients (i.e., those with SI, BI, MI, and HC), and a binary logistic regression analysis was performed on the levels of four metabolites. The formula was Logit(P)=Log(P / (1-P))=-3.908 +3239.837H+10262.915I+46092.934J+8684.127K), with a cutoff value of 0.2 for predicting P. When P≥0.2, the patient was classified as a KI patient, and when P<0.2, the patient was classified as a non-KI patient. Where H represents the serum content of 2-hydroxyethanesulfonate; I represents the serum content of 5,8,11-eicosatotrienoic acid; J represents the serum content of pyrazine; and K represents the serum content of phosphatidylethanolamine (18:1e / 21:2).

[0019] In the aforementioned application, SI patients were compared with non-SI patients (i.e., those with KI, BI, MI, and HC). A binary logistic regression analysis was performed using the levels of four metabolites, yielding the formula Logit(P) = Log(P / (1-P)) = 1.028 – 115813.522L – 32060.855M. The cutoff value for predicting P was 0.21. Patients with P ≥ 0.21 were classified as SI patients, and those with P < 0.21 were classified as non-SI patients. Where L represents the serum triglyceride (12:3 / 21:3 / 21:3) content, and M represents the serum L-isoleucine content.

[0020] In the aforementioned application, BI patients were compared with non-BI patients (i.e., those with KI, SI, MI, and HC), and a binary logistic regression analysis was performed using five metabolite levels, yielding the formula Logit(P) = Log(P / (1-P)) = -0.623. +19557.858N+20475.52O-89054.642Q-71464.11R-28455.578S, the cutoff value for predicted P is 0.05. When P ≥ 0.05, the patient is classified as a BI patient, and when P < 0.05, the patient is classified as a non-BI patient; where N represents the serum content of cis-5-tetradecanoylcarnitine, O represents the serum content of lysophosphatidylcholine (22:6(4Z,7Z,10Z,13Z,16Z,19Z)), Q represents the serum content of phosphatidylcholine (14:0 / 18:2), R represents the serum content of phosphatidylethanolamine (10:0 / 26:4), and S represents the serum content of phosphatidylcholine (16:1e / 18:2).

[0021] In the aforementioned application, MI patients were compared with non-MI patients (i.e., those with KI, SI, BI, and HC levels) using a binary logistic regression analysis combining the levels of six metabolites. The resulting formula was Logit(P) = Log(P / (1-P)) = 2.162 – 21062.085T – 51754.546U + 15220.774V – 60136.617W + 580055.017X – 4990.361Y), with a cutoff value of 0.14 for predicting P. When P ≥ 0.14, the patient was classified as an MI patient; when P < 0.14, the patient was classified as an MI patient. At 0.14, patients were classified as non-MI patients, where T represents the serum content of L-α-aspartic acid-L-hydroxyproline; U represents the serum content of phosphatidylethanolamine (16:0 / 18:2); V represents the serum content of phosphatidylcholine (15:0 / 18:2(9Z,12Z)); W represents the serum content of phosphatidylcholine (16:0 / P-16:0); X represents the serum content of phosphatidylcholine (5:0 / 26:1); and Y represents the serum content of phosphatidylcholine (22:5(4Z,7Z,10Z,13Z,16Z) / 14:0).

[0022] The present invention also provides a formulation for diagnosing SLE patients with different organ involvement phenotypes, including a product for detecting the levels of metabolites in the blood in conjunction with the above-described application method.

[0023] The specific steps for obtaining serum diagnostic markers for SLE, SLE with different disease activities, and SLE involving different organs are as follows:

[0024] 1. Collection and analysis of serum samples from SLE patients and HC patients

[0025] (1) Collect blood samples from SLE patients and HC patients, and establish and organize a blood sample standard library;

[0026] (2) Collect and organize clinical information and clinical data of SLE patients and HC to confirm the subjects to be enrolled.

[0027] 2. Processing and testing of subject biological samples

[0028] (1) Participants’ blood samples were collected in vacuum blood collection tubes, coagulated at room temperature, and centrifuged at 3000 rpm for 10 minutes at 4°C. The supernatant was transferred into two 1.5 ml EP tubes and stored at -80°C under liquid nitrogen until used for metabolomics and lipidomics assays.

[0029] (2) Metabolite extraction and LC-MS / MS metabolomics detection;

[0030] (3) Lipid extraction and LC-MS / MS lipidomics detection;

[0031] 3. Data processing and integration

[0032] (1) After the raw data is converted into mzXML format by ProteoWizard software, peak identification, peak extraction, peak alignment and integration are performed using the R program package (XCMS kernel).

[0033] (2) The area normalization process is used to obtain the combined positive and negative ion data, and the content of different metabolites of each sample is used for subsequent analysis.

[0034] 4. Screening of biomarkers

[0035] (1) After preprocessing and integration, the data were analyzed using univariate statistical analysis, including the Student's t-test and orthogonal partial least squares discriminant analysis (OPLS-DA) and other multivariate statistical methods.

[0036] (2) By using the variable importance in the projection (VIP) of the first principal component of the OPLS-DA model > 1, combined with the significance parameter P value of the t test < 0.05, and the relevant threshold of fold change (FC), differential metabolites and lipids between the two groups were identified.

[0037] 5. Screening of biomarker combinations

[0038] (1) Combining the results of single-variable and multivariate statistical analysis, the t-test was used to compare the statistical differences between groups. The supervised analysis of OPLS-DA maximized the global metabolic changes between groups. Differential metabolites and lipids were screened out, a binary logistic regression model was established, the optimal combination of metabolites was designed, and the sensitivity and specificity were determined by AUC and cutoff value to evaluate the overall performance of each biomarker model.

[0039] The beneficial effects of this invention;

[0040] This invention is the first to use screened metabolites or lipids as biomarkers to screen patients with SLE, SLE with different disease activities, KI, SI, BI, and MI. Our study successfully identified biomarkers associated with SLE patients and different clinical phenotypes of SLE, which helps to more accurately diagnose and assess the progression of SLE.

[0041] Abbreviations and Related Metabolites of this Invention (English-Chinese Glossary)

[0042] Systemic lupus erythematosus (SLE)

[0043] Healthy control (HC)

[0044] Systemic lupus erythematosus patients with only kidney involvement (KI)

[0045] Systemic lupus erythematosus patients with only skin involvement (SI)

[0046] Systemic lupus erythematosus patients with only blood system involvement (BI)

[0047] Systemic lupus erythematosus with multisystem involvement (MI)

[0048] Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA)

[0049] Variable Importance in the Projection (VIP) of the First Principal Component in the OPLS-DA Model

[0050] Between-group difference fold: Fold change (FC)

[0051] Liquid chromatography-tandem mass spectrometry (LC-MS / MS)

[0052] Fatty acid (FA)

[0053] Lysophosphatidylcholine (LPC)

[0054] Phosphatidylcholine (PC)

[0055] Phosphatidylethanolamine (PE)

[0056] Triacylglycerol (TAG) Attached Figure Description

[0057] Figure 1 Metabolomics and lipidomics analyses of the OPLS-DA models in the SLE and HC groups yielded scatter plots and model permutation test results.

[0058] Figure 2 : Lipidomics analysis of active SLE and inactive SLE using the OPLS-DA model yielded scatter plots and permutation test results;

[0059] Figure 3 Metabolomics and lipidomics analysis of KI patients in the OPLS-DA model of the HC group yielded scatter plots and model permutation test results.

[0060] Figure 4 Metabolomics and lipidomics analysis of SI patients in the OPLS-DA model of the HC group yielded scatter plots and model permutation test results.

[0061] Figure 5 Metabolomics and lipidomics analysis of BI patients in the OPLS-DA model of the HC group yielded scatter plots and model permutation test results.

[0062] Figure 6 Metabolomics and lipidomics analysis of MI patients in the OPLS-DA model of the HC group yielded scatter plots and model permutation test results.

[0063] Figure 7 Venn diagram of differential metabolites and lipids in four groups of SLE (KI, SI, BI, MI) and HC groups with different organ involvement;

[0064] Figure 8 The scatter plot shows the levels of dehydroepiandrosterone sulfate, 2-methylbutyrylglycine, benzoic acid, and FA (20:1) between SLE and HC.

[0065] Figure 9 The scatter plot shows the LPC (18:0), PC (18:3 / 18:3), and PE (16:0 / 22:4) levels between active SLE and inactive SLE.

[0066] Figure 10The scatter plot shows the levels of 2-hydroxyethanesulfonate, 5,8,11-eicosatotrienoic acid, pyrazine, and PE (18:1e / 21:2) between KI and HC patients.

[0067] Figure 11 Scatter plots show the levels of L-isoleucine and TAG (12:3 / 21:3 / 21:3) between SI patients and HC patients.

[0068] Figure 12 Scatter plots show the levels of cis-5-tetradecanoylcarnitine, PC (14:0 / 18:2), PC (16:1e / 18:2), PE (10:0 / 26:4), and LPC (22:6(4Z,7Z,10Z,13Z,16Z,19Z)) between BI patients and HC patients.

[0069] Figure 13 Scatter plots show the levels of L-α-aspartate-L-hydroxyproline, PE (16:0 / 18:2), PC (15:0 / 18:2(9Z,12Z)), PC (16:0 / P-16:0), PC (5:0 / 26:1), and PC (22:5(4Z,7Z,10Z,13Z,16Z) / 14:0) between MI patients and HC.

[0070] Figure 14 : ROC curves for comparative diagnosis of each group (A: SLE vs. HC; B: Inactive SLE vs. Active SLE; C: KI vs. SI / BI / MI / HC; D: SI vs. KI / BI / MI / HC; E: BI vs. KI / SI / MI / HC; F: MI vs. KI / SI / BI / HC).

[0071] Figure 15 Retention time of biomarkers for SLE and different levels of SLE activity in a serum sample's TIC plot.

[0072] Figure 16 The retention time of biomarkers of SLE involving different organs in a serum sample according to the TIC plot.

[0073] Total ion chromatogram (TIC). Detailed Implementation

[0074] To further understand this invention, the technical solutions of this invention will be clearly and completely described below in conjunction with the embodiments of this invention. The described embodiments are only a part of the embodiments of this invention, and not all of them. The following description of the embodiments of this invention should not be construed as any limitation on this invention. All other embodiments obtained by those skilled in the art without creative effort are protected by this invention. Unless otherwise stated, all technical and scientific terms used herein have the meanings commonly known to those skilled in the art to which this invention pertains.

[0075] The SLE cases included in this invention were all patients clinically diagnosed with SLE by dermatologists or rheumatologists. All patients met at least four of the American College of Rheumatology (ACR) classification criteria and had no history of other autoimmune diseases. Disease activity was assessed using the SLEDAI. Inactive SLE was defined as a SLEDAI score of 0-4, and active SLE as a SLEDAI score higher than 4. Inclusion criteria for KI patients were based on renal biopsy or the presence of one or more of the following: (a) proteinuria >0.5g / 24h, (b) hematuria (red blood cells >5 / HP, excluding infection and other causes), (c) pyuria (white blood cells >5 / HP, excluding infection and other causes), and (d) urinary casts (hemoglobin, granular casts, or red blood cell casts). Inclusion criteria for SI patients included any one or more of the following: facial discoid erythema, cheek butterfly erythema, rash, and alopecia. Inclusion criteria for BI patients included one or more of the following: (a) white blood cell count <3×10⁻⁶. 9 / L, (b) Platelets <100×10 9 / L; MI patients are defined as having involvement of two or more of the aforementioned systems.

[0076] Healthy control (HC): Physically healthy, without SLE, and without a history of other autoimmune diseases.

[0077] Example: Screening of potential serum biomarkers in patients with SLE, SLE with different disease activities, and patients with different organ involvement (KI, SI, BI, MI).

[0078] I. Obtaining serum samples from SLE patients and HC patients

[0079] This study was approved by the hospital's ethics committee, and informed consent was obtained. Serum samples were collected from 133 SLE patients and 30 HC patients and stored at -80°C until used for metabolomics and lipidomics analysis. The 133 SLE patients were divided into 70 active SLE and 63 inactive SLE based on their SLEDAI scores; further, they were further categorized into four groups based on organ involvement phenotypes: 30 cases of renal system-only SLE (KI), 29 cases of skin system-only SLE (SI), 14 cases of hematologic system-only SLE (BI), and 30 cases of multi-system SLE (MI).

[0080] II. Processing and Detection of Serum Samples from 163 Subjects

[0081] Metabolomics and lipidomics analyses were performed on each sample using LC-MS / MS technology to obtain the original metabolic fingerprint profiles of each serum sample.

[0082] 1. Metabolite extraction and LC-MS / MS metabolomics analysis

[0083] (1) Extraction of metabolites

[0084] Transfer 100 μL of serum sample to an EP tube, add 400 μL of extraction buffer (methanol:acetonitrile = 1:1 (V / V), containing isotope-labeled internal standard mixture), vortex for 30 seconds; sonicate for 10 minutes (ice-water bath); incubate at -40°C for 1 hour; centrifuge the sample at 12,000 rpm (centrifugal force 13,800 (X g), radius 8.6 cm) for 15 minutes at 4°C; collect the supernatant in a sample vial for instrumental analysis; and mix equal amounts of supernatant from all samples to form a QC sample for instrumental analysis.

[0085] (2) LC-MS / MS metabolomics analysis

[0086] This project utilized a Vanquish (Thermo Fisher Scientific) ultra-high performance liquid chromatograph (UHPLC) with a Waters ACQUITY UPLC BEH Amide (2.1 mm × 100 mm, 1.7 μm) column for chromatographic separation of the target compounds. Phase A of the HPLC was aqueous, containing 25 mmol / L ammonium acetate and 25 mmol / L ammonia, while Phase B was acetonitrile. The sample pan temperature was 4 °C, and the injection volume was 2 μL. An Orbitrap Exporis 120 mass spectrometer was used for primary and secondary mass spectrometry data acquisition under the control of software (Xcalibur, version 4.0.27, Thermo). Detailed parameters are as follows: Sheath gas flow rate: 50 Arb, Aux gas flow rate: 15 Arb, Capillary temperature: 320℃, Full ms resolution: 60000, MS / MS resolution: 15000, Collision energy: 10 / 30 / 60 in ECE mode, Spray voltage: 3.8KV (positive) or -3.4KV (negative).

[0087] 2. Lipid extraction and LC-MS / MS lipidomics analysis

[0088] (1) Lipid extraction

[0089] Transfer 100 μL of serum sample and add 480 μL of extraction buffer (MTBE:MeOH = 5:1, containing IS); vortex for 30 s, sonicate in an ice-water bath for 10 min; then incubate the sample at -40℃ for 1 h. Centrifuge the sample at 4℃, 3000 rpm (centrifugal force 900 (×g), radius 8.6 cm) for 15 min, collect 300 μL of supernatant, and vacuum dry; add 100 μL of solution (DCM:MeOH = 1:1) to reconstitute, vortex for 30 s, and sonicate in an ice-water bath for 10 min; centrifuge the sample at 4℃, 13000 rpm (centrifugal force 16200 (×g), radius 8.6 cm) for 15 min; collect 75 μL of supernatant in a sample vial for instrumental analysis; and mix 20 μL of supernatant from all samples to form a QC sample for instrumental analysis.

[0090] (2) LC-MS / MS lipidomics analysis

[0091] This project used an Agilent 1290 (Agilent Technologies) ultra-high performance liquid chromatograph (UHPLC) with a Phenomen Kinetex C18 (2.1*100mm, 1.7μm) column for chromatographic separation of the target compounds. Phase A of the HPLC consisted of 40% water and 60% acetonitrile solution containing 10 mmol / L ammonium formate; Phase B consisted of 10% acetonitrile and 90% isopropanol solution, with 50 mL of 10 mmol / L ammonium formate aqueous solution added per 1000 mL. Gradient elution was employed: 0–1.0 min, 40% B; 1.0–12.0 min, 40%–100% B; 12.0–13.5 min, 100% B; 13.5–13.7 min, 100%–40% B; 13.7–18.0 min, 40% B. Mobile phase flow rate: 0.3 mL / min, column temperature: 55℃, sample tray temperature: 4℃, injection volume: 2 μL for positive ions; 2 μL for negative ions. The Thermo Q Exactive Orbitrap mass spectrometer can perform primary and secondary mass spectrometry data acquisition under the control of software (Xcalibur, version: 4.0.27, Thermo). Detailed parameters are as follows: Sheath gas flow rate: 30 Arb, Aux gas flow rate: 10 Arb, Capillary temperature: 320℃ (positive) or 300℃ (negative), Full ms resolution: 70000, MS / MS resolution: 17500, Collision energy: 15 / 30 / 45in NCE mode, Spray voltage: 5kV (positive) or -4.5kV (negative).

[0092] 3. Data Processing and Integration

[0093] After the raw data was converted to mzXML format using ProteoWizard software, peak identification, peak extraction, peak alignment, and integration were performed using the R package (with XCMS as the kernel). The raw data processing mainly included three aspects: filtering of bias and missing values, imputation of missing values, and data standardization. First, substances with a detection rate less than 50% or a relative standard deviation greater than 30% were filtered. Second, substances that were not detected in some groups due to extremely low concentrations were imputed using numerical simulation to fill in half of the minimum value. Finally, the data was standardized using the internal standard method to obtain standardized positive and negative ion pattern data. This data was then matched with a secondary mass spectrometry database for substance annotation. Area normalization was used to obtain the merged positive and negative ion data, revealing the content of different metabolites in each sample for subsequent analysis.

[0094] 4. Screening of biomarker combinations

[0095] (1) Screening of differential metabolites and lipids

[0096] The data were processed using SIMCA with logarithmic transformation and UV formatting. First, OPLS-DA modeling analysis was performed on the first principal component. OPLS-DA is a supervised pattern recognition method. By setting grouping information, we can filter out orthogonal variables in metabolites that are not related to the categorical variables, and analyze orthogonal and non-orthogonal variables separately, thus obtaining more reliable inter-group differences in metabolites. To verify the quality of the model, 7-fold cross-validation was used; then, the R-value obtained after cross-validation was used... 2 Y (model interpretability for categorical variable Y) and Q 2 The model's effectiveness is evaluated based on its predictability; finally, a permutation test is performed, randomly changing the order of the categorical variable Y multiple times to obtain different random Q values. 2 The Q-value is used to further test the effectiveness of the model. The permutation test of the random model... 2 The values ​​are all less than the Q of the original model. 2 Value; Q 2 The intercept of the regression line on the ordinate is less than zero. Simultaneously, as the retention rate of the permutation gradually decreases, the proportion of the permuted Y variable increases, and the Q of the stochastic model... 2 The value gradually decreased. This indicates that the original model has good robustness and there is no overfitting.

[0097] (2) Screening of biomarkers

[0098] By using the OPLS-DA model with VIP>1, combined with the significance parameter of t-test (P-value <0.05), and the relevant threshold of fold change (FC), differential metabolites and lipids between the two groups were identified. Integrating the differential metabolites and lipids, a diagnostic model was established using binary logistic regression and ROC analysis to obtain the AUC, sensitivity, and specificity values ​​for the corresponding diagnostic groups.

[0099] III. Results

[0100] 1. Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA)

[0101] Metabolomics and lipid profiles of serum samples revealed the differences between SLE and HC. The OPLS-DA models for both SLE and HC serum metabolomics exhibited good robustness and showed no overfitting. Figure 1 The OPLS-DA model for metabolomics in serum samples of active and inactive SLE showed overfitting, while the OPLS-DA model for lipidomics in active and inactive SLE exhibited good robustness. Figure 2 The OPLS-DA models of HC showed good robustness in terms of both metabolomics and lipidomics for SLE with only kidney involvement (KI), only skin involvement (SI), only hematologic system involvement (BI), and multiple system involvement (MI). Figures 3-6 )

[0102] 2. Screening of differentially expressed metabolites

[0103] (1) This invention uses VIP>1, t-test P<0.05, and FC>1.5 or <0.667 in OPLS-DA as screening criteria to screen differentially expressed metabolites and lipids between SLE and HC. A combined model was established using binary logistic regression and ROC analysis. The results showed that the diagnostic model composed of four metabolites—dehydroepiandrosterone sulfate, 2-methylbutyrylglycine, benzoic acid, and FA (20:1)—exhibited the best predictive efficiency. Its AUC was 0.998, sensitivity was 0.987, and specificity was 1.00. (Table 1)

[0104] (2) This invention uses VIP>1, t-test P<0.05, and FC>1.2 or <0.833 in OPLS-DA as screening criteria to screen differentially expressed lipids between active and inactive SLE. A combined model was established using binary logistic regression and ROC analysis. The results showed that the diagnostic model composed of three lipids: LPC (18:0), PC (18:3 / 18:3), and PE (16:0 / 22:4) can be used to distinguish between active and inactive SLE. Its AUC was 0.767, sensitivity was 0.691, and specificity was 0.687 (Table 2).

[0105] (3) This invention uses VIP>1 in OPLS-DA, P<0.05 in t-test, and FC>1.5 or <0.667 as screening criteria. First, it screens out the significantly different metabolites and lipids between KI, SI, BI, MI and HC. Then, it uses Venn diagrams to screen out metabolites and lipids specific to the KI, SI, BI and MI groups. Figure 7 Then, logistic regression and ROC analysis were used to establish a combined model to distinguish between KI patients and non-KI patients, SI patients and non-SI patients, BI patients and non-BI patients, and MI patients and non-MI patients. The four biomarkers used to diagnose KI patients are: 2-hydroxymethylethanesulfonate, 5,8,11-eicosatetrienoic acid, pyrazine, and PE (18:1e / 21:2); the two metabolites used to diagnose SI patients are: L-isoleucine and TAG (12:3 / 21:3 / 21:3); the five metabolites used to diagnose BI patients are: cis-5-tetradecanoylcarnitine, PC (14:0 / 18:2), PC (16:1e / 18:2), PE (10:0 / 26:4), and LPC (22:6(4Z,7Z,10Z,13Z,16Z,19Z)); and the six biomarkers used to diagnose MI patients are: L-α-aspartic acid-L-hydroxyproline, PE(16:0 / 18:2), PC(15:0 / 18:2(9Z,12Z)), PC(16:0 / P-16:0), PC(5:0 / 26:1), PC(22:5(4Z,7Z,10Z,13Z,16Z) / 14:0) (Table 4)

[0106] The actual range of the area under the ROC curve (AUC) is 0.5 to 1. It is generally believed that for a diagnostic test, the diagnostic value is low when the area under the ROC curve is between 0.5 and 0.7, moderate when it is between 0.7 and 0.9, and high when it is above 0.9.

[0107] Compared with HC, SLE patients

[0108] Binary logistic regression analysis was performed on the levels of four metabolites, yielding the formula Logit(P) = -0.794 + 34223.471A + 178897.26B – 2009.888C + 116698.988D. The cutoff value for predicted P was 0.81. When P ≥ 0.81, it was classified as SLE, and when P < 0.81, it was diagnosed as HC. Where A represents the serum content of 2-methylbutyrylglycine; B represents the serum content of benzoic acid; C represents the serum content of dehydroepiandrosterone sulfate; and D represents the serum content of FA (20:1).

[0109] Compared with inactive SLE patients,

[0110] Binary logistic regression analysis was performed using three metabolite levels, yielding the formula Logit(P) = 2.379 - 28127.018E - 37622.689F + 4938.163G. The cutoff value for predicted P was 0.5. When P ≥ 0.5, it was classified as active SLE, and when P < 0.5, it was classified as inactive SLE. Where E represents the serum content of LPC (18:0), F represents the serum content of PC (18:3 / 18:3), and G represents the serum content of PE (16:0 / 22:4).

[0111] Binary logistic regression analysis was performed on KI patients and non-KI patients (SI, BI, MI, HC) using the levels of four metabolites. The formula Logit(P) = -3.908 + 3239.837H + 10262.915I + 46092.934J + 8684.127K was used to predict P. The cutoff value for P was 0.2. Patients with P ≥ 0.2 were classified as KI patients, and patients with P < 0.2 were classified as non-KI patients. Where H represents the serum content of 2-hydroxyethanesulfonate; I represents the serum content of 5,8,11-eicosatotrienoic acid; J represents the serum content of pyrazine; and K represents the serum content of PE (18:1e / 21:2).

[0112] Binary logistic regression analysis was performed on patients with SI (Infectious Disease) and non-SI (Infectious Disease) levels (KI, BI, MI, HC) using four metabolite levels. The formula was Logit(P) = 1.028 – 115813.522L – 32060.855M, with a cutoff value of 0.21 for predicting P. Patients were classified as SI patients when P ≥ 0.21 and as non-SI patients when P < 0.21. Where L represents the serum content of TAG (12:3 / 21:3 / 21:3) and M represents the serum content of L-isoleucine.

[0113] Binary logistic regression analysis was performed on BI patients and non-BI patients (KI, SI, MI, HC) using five metabolite levels, yielding the formula Logit(P) = -0.623 + 19557.858N + 20475.52O - 89054.642Q- 71464.11R-28455.578S, the cutoff value for predicted P is 0.05. When P ≥ 0.05, the patient is classified as a BI patient, and when P < 0.05, the patient is classified as a non-BI patient; where N represents the serum content of cis-5-tetradecanoylcarnitine, O represents the serum content of LPC (22:6(4Z,7Z,10Z,13Z,16Z,19Z)), Q represents the serum content of PC (14:0 / 18:2), R represents the serum content of PE (10:0 / 26:4), and S represents the serum content of PC (16:1e / 18:2).

[0114] Binary logistic regression analysis was performed on MI patients and non-MI patients (KI, SI, BI, HC) using six metabolite levels, yielding the formula Logit(P) = 2.162 – 21062.085T – 51754.546U + 15220.774V – 60136.617W + 580055.017X – 4990.361Y. The cutoff value for predicting P was 0.14; when P ≥ 0.14, patients were classified as MI patients, and when P < 0.14, patients were classified as MI patients. Here, T represents the serum L-α-aspartate-L-hydroxyproline content, and U represents the PE (16:0 / 18:2) ratio. In serum, V represents the content of PC (15:0 / 18:2(9Z,12Z)); W represents the content of PC (16:0 / P-16:0); X represents the content of PC (5:0 / 26:1); and Y represents the content of PC (22:5(4Z,7Z,10Z,13Z,16Z) / 14:0) in serum.

[0115] Table 1. Classification performance of the SLE diagnostic model constructed using four serum biomarkers

[0116] Table 2. Classification performance of the active SLE diagnostic model constructed using three serum biomarkers

[0117] Table 3. Biomarkers specific to SLE groups with different organ involvement (KI, SI, BI, MI)

[0118] Table 4. Classification performance of SLE diagnostic models involving different organs constructed using serum biomarkers

[0119] Table 1. Classification performance of the SLE diagnostic model constructed using four serum biomarkers

[0120]

[0121] Table 2. Classification performance of the active SLE diagnostic model constructed using three serum biomarkers

[0122]

[0123] Table 3. Biomarkers specific to SLE groups with different organ involvement (KI, SI, BI, MI) (NA: indicates that the screening criteria for metabolites or lipids that do not meet the criteria for significant differences between groups are: VIP>1&P<0.05&FC>1.50 or FC<0.667.)

[0124]

[0125]

[0126] Table 4. Classification performance of SLE diagnostic models involving different organs constructed using serum biomarkers

[0127]

Claims

1. Use of a product for detecting the level of a metabolite in serum for the preparation of a diagnostic formulation for SLE patients, characterized in that, The four biomarker combination for diagnosing SLE patients is: dehydroepiandrosterone sulfate, 2-methylbutyrylglycine, benzoic acid and fatty acid (20:1).

2. Use according to claim 1, characterized in that, The level of dehydroepiandrosterone sulfate in SLE patients is lower than that of healthy controls, and the levels of 2-methylbutyrylglycine, benzoic acid and fatty acid (20:1) are higher than those of healthy controls.

3. Use according to claim 1, characterized in that, The product obtains the metabolite levels of multiple samples by liquid chromatography tandem mass spectrometry, screens the differential metabolites and lipids between groups by univariate statistical analysis including Student's t-test and multivariate analysis orthogonal partial least squares discriminant analysis, and then establishes a diagnostic model by using binary Logistic regression and receiver operating characteristic curve for detection of the test sample.

4. Use according to claim 1 or 2 or 3, characterized in that, Compared with HC, binary Logistic regression analysis was performed on the four metabolite levels of SLE patients, and the formula Logit(P) = Log(P / (1-P)) = -0.794 + 34223.471A + 178897.26B - 2009.888C + 116698.988D was obtained, the cutoff value of P was 0.81, when P≥0.81, it was classified as SLE, and when P<0.81, it was classified as HC; wherein A represents the content of 2-methylbutyrylglycine in serum; B represents the content of benzoic acid in serum; C represents the content of dehydroepiandrosterone sulfate in serum; D represents the content of fatty acid (20:1) in serum, and the HC is a healthy control.

5. A preparation for diagnosing SLE patients, characterized by, The product for detecting the metabolite level in blood comprising the application method of any one of claims 1-4. The product for detecting the metabolite level in blood comprising the application method of any one of claims 1-4.

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